Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks.

What the Biden administration’s artificial intelligence executive order means for radiology
“Radiologists themselves can and should play a key role in policy creation at every level," members of the specialty wrote in JACR.
Humana expands use of automated prior authorization software into diagnostic imaging
The Louisville, Kentucky-based payer first partnered with vendor Cohere Health in 2021 to launch a pilot program in 12 states.
Use of AI in radiology revenue cycle management
Dave Walker, senior director of revenue cycle, Radiology Associates of North Texas, explains how his practice uses artificial intelligence for revenue cycle management during the Radiology Business Management Association (RBMA) 2024 meeting.
GPT-4 can detect radiology report errors at the same rate as members of the specialty
“This efficiency in detecting errors may hint at a future where AI can help optimize the workflow within radiology departments," a lead author of the study said.
Semiautonomous AI shows potential to reduce false positives, unnecessary procedures and medical expenses
Scientists developed the deep learning algorithm using a set of over 123,000 digital mammograms (including 6,100-plus cancer cases).

AI in Healthcare Summit to explore applications in robotics, imaging, interoperability

The two-day AI in Healthcare Summit on Thursday and Friday, Jan. 18 and 19, at the Harvard Club in Boston offers an in-depth discussion on the current state of the healthcare AI industry, AI-driven advancements in medical imaging and diagnostics, surgical robotics, patient engagement, integration and interoperability opportunities and challenges, bringing the human aspect back to healthcare, legal considerations and AI’s role in value-based care.

January 12, 2018

vRad announces new patent for escalating radiology procedures through AI

vRad, a MEDNAX company, announced this week that it has secured a patent for using artificial intelligence (AI) to escalate high-priority radiology procedures.

January 10, 2018

Researchers use machine learning to detect fractures in plain radiographs

Machine learning using deep convolutional neural networks (CNNs) can be used to detect fractures in plain radiographs, according to a new study published in Clinical Radiology.

December 20, 2017

Cheryl Petersilge, MD, MBA, with the department of regional radiology at the Cleveland Clinic, examined enterprise imaging—and how radiologists must integrate and collaborate with other departments. Her clinical perspective clinical perspective was published online in the October issue of the American Journal of Roentgenology.

Software using machine learning algorithms accurately audits radiologist compliance

Imaging groups throughout the United States have moved to standardized radiology reports in recent years, and it’s a trend that continues to pick up steam. One side effect of this change is that leaders must then perform long, labor-intensive manual audits of their team’s reports to confirm compliance. But what if groups could somehow perform an automated audit, making those pesky manual audits a thing of the past?

December 19, 2017

GE and NVIDIA Join Forces to Accelerate Artificial Intelligence Adoption in Healthcare

GE Healthcare and NVIDIA today announced they will deepen their 10-year partnership to bring the most sophisticated artificial intelligence (AI) to GE Healthcare’s 500,000 imaging devices globally and accelerate the speed at which healthcare data can be processed.

November 28, 2017

artificial intelligence machine deep learning

Just the beginning: 6 applications for machine learning in radiology beyond image interpretation

Discussions about machine learning’s impact on radiology might begin with image interpretation, but that’s only the tip of the iceberg. When it comes to realizing the technology’s full potential, it’s like Bachman Turner Overdrive sang many years ago: You ain’t seen nothing yet.

November 17, 2017

ICAD RECEIVES FDA APPROVAL FOR POWERLOOK TOMO DETECTION

First to market with innovative digital breast 3D tomosynthesis cancer detection and workflow solution, built on deep learning technology that improves efficiency and reduces reading time for radiologists

March 27, 2017

First Patient Treated With LC Bead LUMI™ Radiopaque Embolic Bead Supported by Philips Live Image Guidance

Collaboration brings new tools to interventional radiologists and oncologists to help treat liver cancer and enhanced patient care.

February 3, 2016

Around the web

Cardiovascular Business

Medicare drops AUC requirement for advanced imaging, ASNC celebrates

"This was an unneeded burden, which was solely adding to the administrative hassles of medicine," said American Society of Nuclear Cardiology President Larry Phillips.

Cardiovascular Business

Cardiology, radiology groups urge insurance provider to rethink imaging policy

SCAI and four other major healthcare organizations signed a joint letter in support of intravascular ultrasound.

Cardiovascular Business

Radiology AI company gains FDA clearance for new CT offerings focused on PE, stroke

The newly approved AI models are designed to improve the detection of pulmonary embolisms and strokes in patients who undergo CT scans.

Artificial Intelligence

What the Biden administration’s artificial intelligence executive order means for radiology

Humana expands use of automated prior authorization software into diagnostic imaging

Use of AI in radiology revenue cycle management

GPT-4 can detect radiology report errors at the same rate as members of the specialty

Semiautonomous AI shows potential to reduce false positives, unnecessary procedures and medical expenses

Around the web